System and method for energy management

ABSTRACT

Embodiments of the present invention assist customers in managing the four types of energy assets, that is, generation, storage, usage, and controllable load assets. Embodiments of the present invention for the first time develop and predict a customer baseline (“CBL”) usage of electricity, using a predictive model based on simulation of energy assets, based on business as usual (“BAU”) of the customer&#39;s facility. The customer is provided with options for operating schedules based on algorithms, which allow the customer to maximize the economic return on its generation assets, its storage assets, and its load control assets. Embodiments of the invention enable the grid to verify that the customer has taken action to control load in response to price. This embodiment of the invention calculates the amount of energy that the customer would have consumed, absent any reduction of use made in response to price. Specifically, the embodiment models the usage of all the customer&#39;s electricity consuming devices, based on the customer&#39;s usual conditions. This model of the expected consumption can then be compared to actual actions taken by the customer, and the resulting consumption levels, to verify that the customer has reduced consumption and is entitled to payment for the energy that was not consumed.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority of U.S. provisional patentapplication No. 61/279,589, filed on Oct. 23, 2009 entitled “VPower™System,” by Alain Pierre Steven. The entire disclosure of thisprovisional patent application is incorporated herein by this reference.

FIELD OF THE INVENTION

Embodiments of this invention relate to systems and methods of energymanagement. More specifically, embodiments of this invention relate tothe efficient use, management and measurement of electricityconsumption.

BACKGROUND OF THE INVENTION

The maintenance of reliable electric service over the power gridrequires power grid operators to ensure that the production of energy byelectrical generators (supply) on the grid is constantly balancedagainst the consumption of electricity on the grid (consumer demand).

In the United States regulatory responsibility for the power grid isprimarily held by the Federal Energy Regulatory Commission (FERC).Operational responsibility for developing and operating a balancedelectric grid is held by individual utilities or independent regionalgrid operators known in the industry as Independent System Operators(“ISO's”) or Regional Transmission Organizations (“RTO's”). There arecurrently seven RTOs/ISO's operating in the United States. Collectivelythese entities service states and regions that represent a substantialportion of national power consumption.

In areas served by RTO's/ISO's, FERC has required that in addition tomanaging the operation of the power grid, the RTO's and ISO's mustmanage the price of power generated and consumed on the grid usingpricing principles that value the price of energy at the instantaneousintersection of supply and demand. To do this, the RTO's use pricingauctions. These price auctions, in addition to setting the price ofelectricity, must also obtain enough electricity production for the gridat the correct locations to ensure that the grid is capable ofdelivering adequate energy to the location of demand on the distributiongrid. This process establishes the Locational Marginal Price (“LMP”) forthe next incremental energy production resource at a location.

In order to achieve these objectives the RTO employs a process know asSecurity Constrained Economic Dispatch. This process accepts generationoffers in sequence from the lowest priced offer to the highest pricedoffer, up to an amount needed to satisfy load conditions. Thiseconomically efficient method is constrained, however, by the physicallimitations of the grid to transmit power from where it is generated towhere it is needed. Thus, if a low cost generating unit would be usedbased on price, but use of that unit would overload a transmission lineand exceed its safe limits (for example, for voltage and thermal loadinglimits), then that unit will not be used. Instead, a more expensive unitwhich, due to its location, will not jeopardize the reliability ofelectric service, will be used. The first aspect of the processdescribed above (the “economic dispatch”) is achieved by a series ofmathematical algorithms which choose the generating units to bedispatched so as to minimize production cost. Prices are thenestablished on a locational basis with the prices in different locationsreflecting the cost of the highest priced generator actually needed toprovide service to a given location. The process of assuring locationalprices uses a series of mathematical algorithms over energy managementsystems.

Historic Use of Load on the Grid

Historically, reliability of the grid was maintained by command andcontrol measures under which the RTO could physically change the flow ofenergy on the grid if necessary to preserve reliability and avoidblackouts. Generation was increased or decreased as needed and load(demand) was presumed to be inflexible. This process did not rely uponprice signals to maintain reliability. Therefore, it did not lead toeconomically efficient results while preserving reliability.

Today, grid operators recognize that load will respond to price signalsand thus will pay consumers to curtail their consumption of electricity.This payment reflects the value that such curtailment provides to thegrid in terms of enhanced reliability, and more efficient prices forelectricity.

Market rules now permit customers to make offers to curtail theirelectricity use directly to the grid operator. These offers will beaccepted so long as the price is lower than the competing offer byanother resource, such as a generator. If accepted, the customer willcurtail its use pursuant to the offer accepted by the grid and be paidaccordingly.

This process enhances reliability by insuring that the grid is incontinuous physical balance; that is, supply and demand are continuouslymatched. Moreover, it achieves this objective in the most economicallyefficient method possible, by accepting offers to curtail use when theseoffers are of lower cost than alternative means of maintaining balance(i.e., buying more supply). This mechanism benefits society as a wholeby reducing the cost of electricity. And of course this process benefitsindividual customers by enabling them to control their individualelectricity bills.

Policies in Favor of Reduction of Demand

Regulatory authorities and legislatures have begun to adopt policiesintended to facilitate load control by customers (i.e., reduction ofenergy use by customers). These policies have been adopted inrecognition of the fact that load control is in the public interest. Forexample, these policies provide compensation to customers who provideload control because load control reduces average prices, maintainsreliability of the grid, and assists in meeting environmentalobjectives.

The price of electricity is set in the markets by the marginal cost,that is, the cost of the highest priced source which is needed tosatisfy demand. More expensive sources are not used and are not paid.Demand reduction has the effect of reducing the amount of demand thatmust be satisfied and therefore reduces the price of electricity, asmore expensive sources are not needed or called upon.

The price of electricity on wholesale markets changes every 15 minutes.That is, every 15 minutes different resources are used to satisfydemand, based on the economic dispatch described above.

Consequently, a need exists for improved systems to control and reduceelectricity use by a customer, and to measure and price that reduction.

DESCRIPTION OF THE FIGURES

The features and advantages of embodiments of the present invention canbe understood by reference to the following detailed description takenwith the following figures of embodiments of the invention.

FIG. 1 shows an embodiment of a method of the present invention.

FIG. 2 shows further embodiments of a method of the present invention.

FIG. 3 shows a general overview of an embodiment of one possiblealgorithm of the present invention.

FIG. 4 shows system modules for embodiments of the invention.

FIG. 5 shows an optimization example with optimization options and 4cases, for an embodiment of the invention.

FIG. 6 shows system architecture for embodiments of the invention thatsupport and implement the system modules as shown in FIG. 4, and produceoptimization examples and options as shown in FIG. 5.

FIG. 7 shows power use and price information for various electric energyresources in an optimization case example over a 24-hour cycle asgenerated by system modules and architecture as shown in FIG. 4, FIG. 5,and FIG. 6.

DESCRIPTION OF VARIOUS EMBODIMENTS

Embodiments of the present invention assist customers in managing thefour types of energy assets, that is, energy generation, energy storage,energy usage, and energy load control assets. The customer is providedwith information, options and operating schedules based on thealgorithms herein, which allow the customer to maximize the economicreturn on his generation assets, his storage assets, and his loadcontrol assets. This process is referred to as optimization. Embodimentsof the present invention focus on electric power and the use,consumption, production, distribution, demand, and load for electricity.

Embodiments of the present invention for the first time develop andpredict a customer baseline (“CBL”) usage of electricity, using apredictive model based on simulation of energy assets, based on businessas usual (“BAU”) of the customer's facility. This simulation approach isdifferent from and more accurate than previous methods that merely usedhistorical usage data at the customer account level from the customer'sgeneral electric meter, by statistical trend projection of this data.

Load control assets (also called controllable load assets) are assetsthat give a customer the ability to control the consumption ofelectricity by electricity-using devices. For example, a customer cancontrol its use of electricity for air conditioning, lighting, orproduction processes, by use of variable frequency drive motors, smartthermostats, or other equipment. Load control assets may include typesof energy generation, storage and usage assets. Load control assets mayalso refer to the energy management systems or building managementsystems which control operation of the electricity consuming devices.

Embodiments of the invention enable businesses to plan their energyconsumption, and to plan their curtailments of energy consumption inresponse to price signals, such that the customer can optimize its useof electricity and earn revenue from the interstate electric grid bycurtailing the user's consumption of electricity. Further embodimentsrelate to integrating customer-owned distributed resources including acustomer's ability to curtail usage in response to price signals forenergy generation, storage and consumption from the electric grid.Further, the invention enables the user to actively manage its electricload and to control its electric bill. Finally, the invention allows theuser to optimize the financial value of its assets including storage,generation, consumption, and its ability to curtail consumption.

Embodiments of the present invention further the objective of maximizingeconomically efficient operation of the grid by facilitating demandreductions by customers in response to price. This is distinct fromprior methods which would have curtailed usage via command and controlof the grid by the RTO with no reference to price. The value of realtime demand reduction for the electric grid is enhanced reliability,more efficient pricing and individual consumer value.

Embodiments of the present invention include a decision tool which willcommunicate price information and load control options to a customerseveral days in advance of the need to make a decision, and also in realtime, so that the customer can make informed decisions regarding itsconsumption and load control possibilities.

Integration of optimized electricity resources and assets does not existtoday. Embodiments of the present invention, in effect, convert demandinto a controlled element of the grid.

Embodiments of the present invention permit the real time and day aheadintegration of customer owned electricity resources onto the nationaland regional electric grids. As described above, the installation ofthese customer owned assets is expanding rapidly. However, there is atpresent no system which optimally deploys these resources. In contrast,the RTO's do employ mechanisms which optimally deploy utility ownedgeneration.

Embodiments of the present invention fill a critical need in thenation's electric infrastructure by enabling customers to use theirresources in the most economically advantageous manner. Embodiments ofthe invention turn controlled demand into a resource that the grid canrely upon and dispatch to maintain a physical balance of electricitysupply and demand on the grid. Load is turned into a controllableelement which can be called upon by the grid to adjust its behavior,rather than treated as an immutable fact which cannot be controlled.

Embodiments of the invention enable the grid to verify that the customerhas taken action to control his load in response to price. This isimportant because payment should only be made for verified reductions inelectricity use. This embodiment of the invention calculates the amountof energy that the customer would have consumed absent any reductiontaken in response to price. Specifically, the embodiment models theusage of all the customer's electricity consuming devices, based on thecustomer's business as usual conditions. For example, the invention willmodel the customer's air conditioning usage based upon building thermalproperties, ambient temperature and humidity conditions, and buildingoccupancy. Similarly, the consumption of motors can be modeled based onthe characteristics of the motors. This model of the expectedconsumption can then be compared to actual actions taken by thecustomer, and the resulting consumption levels, to verify that thecustomer has reduced consumption and is entitled to payment for theenergy that was not consumed, but was normally expected to be consumedaccording to the modeling. This saved energy is sometimes called“negawatts” delivered to the grid (as opposed to “megawatts” used fromthe grid).

In one aspect, the invention may be a software networking device thatconverts resources (controllable load, generation, or storage devices)into an integrated system that uses a set of mathematical algorithms forpricing and payments that will vary based on the resource andrequirements of the load and the assets in question.

Architecture of the Embodiments

The architecture of embodiments of the invention consists of severallayers. At the core of the integrated system and processes is thesoftware. The software receives several key inputs associated with theload and with conditions on the grid. Among the important inputs areambient temperature and humidity, because these factors heavilyinfluence the amount of electricity consumed in a building. Similarly,building occupancy is a key input for the same reason. If the resourcebeing controlled is an industrial process, then the consumptioncharacteristics of electricity consuming devices such as motors will beinput to the software. Forecast electricity prices in the wholesalemarket are another important input.

The output of the software is an operating schedule for the end-usersresources, i.e., his load consuming devices, his storage assets, and hisgeneration assets. The output can best be described as an advanceddecision making tool which takes all the inputs, and any operatingconstraints the customer must observe, and provides the customer with aschedule of operations for the next day which allows the customer tooptimize the use of and return on his assets.

The output will be a schedule telling the customer what actions to take,and when to take them. For example, the system output may tell thecustomer to change the temperature setting in his building from 72degrees to 74 degrees, and to do so between the hours of 4 P.M. and 6P.M. Similarly, the output may tell the customer to start his dieselgenerator at 3 P.M. and run it until 5 P.M.

The next layer of the architecture is a communications system includinginterfaces by which Viridity Energy, Inc. (“Viridity”), conveys to thegrid operator the schedule of operations of the customer for the nextday. This is critical to the integration function because the gridoperator must have accurate knowledge of the resources available to itso as to maintain a continuous balance between load and supply.

The next layer of the architecture involves the settlement or billingprocess. The system will provide an invoice to the grid operator,quantifying the reduction in load delivered by the customer reflectingthe time of day of the reduction and the prevailing price at that time.The system will verify the billing amounts and handle settlementsbetween the customer, Viridity, and the grid operator.

The next layer in the architecture involves the monitoring andverification of the actions taken by the customer to reduce load. Thisprocess requires the calculation of a customer base line, which can bedefined as the quantity of electricity the customer will consume underbusiness as usual conditions, that is, the consumption which will occurif the customer takes no action to reduce load in response to price.Embodiments of the invention perform this calculation based uponmathematical modeling of the electricity consumption which would occurin a given building or other electricity consuming device given thethermal and other key characteristics of the building or device. Thealgorithms which are used in the “baseline calculation” are discussedherein.

In accordance with embodiments of the invention, optimization ofefficiency in the use of electric energy is achieved by a process thatincludes establishment of a mathematical simulation model of anenergy-consuming facility. That model uses data concerning variations inthe rates charged by the provider, and other data such as weather data,to control one or more electric energy consuming units in the facility.

More particularly, in accordance with embodiments of the invention,electric energy from a provider is delivered through a utility powergrid to an energy-consuming facility having plural energy-consumingunits. A mathematical model is established, representing the usage ofelectric energy by the energy-consuming facility. The model, which isindependent of variations in the price charged by the electricityprovider to the facility, is stored in a computer memory. On the basisof the mathematical model and variations in the price charged to thefacility for electric energy, a computer is used to run multiplescenarios to determine a plan for controlling the operation of one ormore of the facility's energy-consuming units to reduce the provider'soverall charge to the facility for electric energy. Control signals aregenerated automatically, based on the plan, from electronic data derivedfrom the computer operation. The control signals are then used tocontrol one or more energy-consuming units so that their operationconforms to the plan.

The determination of a plan for controlling the operation of at leastone of the facility's plural energy-consuming units can also be based onweather prediction data incorporated into the program.

The control signals can control the timing of the operation of one ormore of the facility's plural energy-consuming units. For example, inthe case of an air conditioner, the control signal can time theoperation of the air conditioner so that it pre-cools the building thatit serves during a time when the price of electric energy is low, e.g.,at night, thereby reducing the consumption of electric energy during theday (when prices are high) and reducing the overall consumption ofelectric energy and the cost of energy to cool the building.

The process can be used, for example, where at least one of thefacility's electric energy consuming units is an air conditioner.

If the facility includes an energy storage device and a solar generator,then the solar generator can be controlled by a control signal. Forexample, the control signal can cause the energy storage device to becharged by the solar generator during a time when the price of energycharged to the facility by the energy provider is below a predeterminedlevel. A control signal automatically generated from electronic dataderived from the program can also control the energy storage device todischarge stored electric energy to the utility power grid when theprice of energy charged to the facility by the energy provider is abovethat predetermined level. A solar generator is always generatingelectricity, whenever there is natural light. The resulting power can beused to charge an energy storage device, or the power can be directlyused by an energy-consuming facility. Embodiments of the invention allowthe owner of these resources to make the optimal financial decisionbetween these two choices.

Model of the Customer Baseline Usage

One aspect of embodiments of the invention is the establishment of a“customer baseline”. The customer baseline is the level of a facility'sconsumption of electric energy on a given day, without regard to anyaction taken in response to price. That is, the customer baselinecorresponds to the customer's energy consumption resulting frombusiness-as-usual operation of its facility. Instead of relying onhistorical energy consumption data, the calculation of the customerbaseline is based upon a computer simulation model of a facility'senergy consumption, taking into account its operating plans. Thus, thecalculation is predictive rather than backward-looking.

A mathematical simulation model is developed for each facility, takinginto account the facility's energy consuming equipment and its operatingplans shown as model builder module 502 in FIG. 5 herein. Thecalculation of consumption for each day may reflect all of the knownrelevant variables, such as building materials, thermal properties ofthe building or buildings, building occupancy, desired temperature,ambient temperature, and operations for the day. At least the followingparameters may be included: HVAC building settings and controls,identification of interruptible loads and their pre-defined response toa price signal, desired temperatures. The software retains a record ofthe actual demand response action taken by an end-user.

The model for a facility can be composed of a group of sub-models of allthe facility's energy-consuming elements, shown in components library503, in FIG. 4 herein. The examples below refer to a building load, butthe same method can be applied to any device, such as a motor orlighting fixture. Variable load as well as interruptible load equipmentmay be modeled specifically. Items constituting a fixed load may bemeasured and modeled individually.

Building thermodynamics is important in the determination of electricalloads. The fundamental thermodynamic equations for this application are:

$\begin{matrix}{{{CpM}\frac{T}{t}} = {{Q\_ in} + {Q\_ body} - {Q\_ hvac} - {Q\_ chill} - {Q\_ vent}}} & (1)\end{matrix}$

Where:

-   -   CpM=Energy-temperature change ratio.    -   Q_in=Energy inflow due to the difference between building        internal temperature and ambient temperature.    -   Q_body=Energy emission from people and equipment in the        building.    -   Q_hvac=Energy inflow from the HVAC in addition to the chiller        and ventilation.    -   Q_chill=Energy inflow from the chiller.    -   Q_vent=Energy inflow from the ventilation system.

Q_in=UA_build×(T ^(A) −T ¹)  (2)

Where:

Q_in=Energy inflow due to the difference between building internaltemperature and ambient temperature.

UA_build=Heat energy transfer rate due to temperature differences.

T^(A)=Ambient temperature outside the building.

T¹=Internal temperature of the building.

Q_body=Num_people×(Q_per_body+(equip_rate×Q_equip))  (3)

Where:

-   -   Q_per_body=Amount of energy emitted per hour from a person        present in the building.    -   Num_people=Number of people present in the building.    -   equip_rate=Rate of personal electronic equipment per person.    -   Q_equip=Average amount of energy emitted by electronic equipment        such as cell phones and computers.

$\begin{matrix}{{Q\_ hvac} = {\frac{u\_ hvac}{k\_ const} \times {MaxQ\_ hvac} \times \left( {T^{1} - {T\_ cool}} \right)}} & (4)\end{matrix}$

Where:

-   -   Q_hvac=Energy inflow from the HVAC in addition to the chiller        and ventilation.    -   u_hvac=HVAC loading.    -   MaxQ_hvacMaximum thermal production capacity of the HVAC.    -   T_cool:=Defines the threshold temperature below which the HVAC        system operates in heating mode, while above which it operates        in cooling mode.    -   T¹=Internal temperature of the building.    -   k_const=Constant parameter.

Q_vent=MaxQ_vent×U_vent  (5)

Where:

Q_vent=Energy inflow from the ventilation system.

u_vent=HVAC ventilation loading.

MaxQ_vent=Maximum thermal production capacity of the HVAC ventilation.

$\begin{matrix}{{Q\_ chill} = {{\frac{u\_ chill}{k\_ const} \times {MaxQ\_ chill} \times \left( {T^{1} - {T\_ cool}} \right)} + {{u\_ dice} \times {Ice\_ drate} \times {Btu\_ MWh}{\_ ConvRate}}}} & (6)\end{matrix}$

Where:

-   -   Q_chill=Energy inflow from the chiller.    -   u_chill=HVAC chiller loading.    -   u_dice=Cooling use of stored ice.    -   MaxQ_chill=Maximum thermal production capacity of the HVAC        chiller.    -   Ice_drate=Chiller's ice-consuming rate when the stored ice is to        be used (discharge).    -   T¹=Internal temperature of the building.    -   k_const=Constant parameter.    -   Btu_MWh_ConvRate=Conversion coefficient of electric to heat        energy.    -   T_cool=Defines the threshold temperature below which the HVAC        system operates in heating mode, while above which it operates        in cooling mode.        The thermodynamic equations are related to electrical load        through the following equations.

$\begin{matrix}{{MW\_ hvac} = {\frac{MaxQ\_ hvac}{{Eff\_ hvac} \times {Btu\_ mWh}{\_ ConvRate}} \times {u\_ hvac}}} & (7)\end{matrix}$

Where:

-   -   MW_hvac=Average HVAC power consumption.    -   Eff_hvac=Efficiency coefficient of HVAC thermal energy        production by electric energy.        -   Btu_Mwh_ConvRate=Conversion coefficient of electric to heat            energy.        -   MaxQ_hvac=Maximum thermal production capacity of the HVAC.        -   U_hac=HVAC loading.

$\begin{matrix}{{MW\_ vent} = {\frac{MaxQ\_ vent}{{Eff\_ vent} \times {Btu\_ mWh}{\_ ConvRate}} \times {u\_ vent}}} & (8)\end{matrix}$

Where:

MW_vent:=Average ventilation power consumption.

MaxQ_vent=Maximum thermal production capacity of the HVAC ventilation.

Eff_vent=Efficiency coefficient of HVAC thermal energy production byelectric energy.

Btu_MWh_ConvRate=Conversion coefficient of electric to heat energy.

$\begin{matrix}{{MW\_ chill} = {{\frac{MaxQ\_ chill}{{Eff\_ chill} \times {Btu\_ mWh}{\_ ConvRate}} \times {u\_ chill}} + {{u\_ cice} \times {Ice\_ crate}}}} & (9)\end{matrix}$

Where:

MW_chill=Average chiller power consumption.

MaxQ_chill=Maximum thermal production capacity of the HVAC chiller.

Eff_chill=Efficiency coefficient of the HVAC chiller.

u_cice=Ice-making operation.

Ice_crate=Chiller's ice-making rate when making ice (charge).

u_chill=HVAC chiller loading.

Btu_MWh_ConvRate=Conversion coefficient of electric to heat energy.

Load_hvac=MW_hvac+MW_vent+MW_chill  10)

Where:

Load_hvac=Total electric power to operate the HVAC system.

MW_hvac=Average HVAC power consumption.

MW_vent=Average ventilation power consumption.

MW_chill=Average chiller power consumption.

The variables Num_people and equip_rate in equation (3) are determinedfrom occupancy data and facility or industry data concerning the numberand types of electronic equipment per person. MaxQ_hvac, MaxQ_chill, andMaxQ_vent, can be determined from equipment name plate data.

The model established for an energy-consuming facility, and thesub-models for related equipment may take into account the abovethermodynamic load equations.

There is a wide variety of other types of variable load andinterruptible load equipment that can be accurately modeled using asimilar approach. The granularity of these models will be that necessaryfor accurate calculation of the business as usual load of a facility asmeasured against the metered load. Sub-metering can be used as necessaryto validate mathematical models of variable and interruptible loads.

For large scale facilities consisting of multiple buildings or elements,the total customer base line load is simply the sum of the base lineloads for the individually-modeled aggregated elements.

The process for developing the model of a facility is iterative. Ageneric, or representative, model of the facility is first developed.Experiments are then performed on the building or buildings to determinetheir thermal parameters. Sub-metering is used as previously mentioned.The experimental results and load data are incorporated to modify andtune the generic model.

After the model is created, it is validated by conducing simulations ofthe facility using the model to calculate the electrical load. Modeloutput is compared to actual historical metered loads. Furtherexperimentation, data collection, and model refinement is performeduntil the model accurately reproduces metered data.

A model can include various types of local energy generation devicessuch as diesel, combined cycle, solar, and wind-powered devices, as wellas any energy storage devices such as ice makers, chilled waterreservoirs, batteries, and flywheels. The model can also include thehourly energy required to store energy, time-variant losses of energy,and delayed use of stored energy for cooling, heating or conversion toelectricity.

A simple type of conversion to consider is a battery system. Energy ispurchased and stored during off-peak periods during which it can bepurchased at lower rates. The stored energy is withdrawn during peakhours (when prices are high), providing a corresponding decrease in thefacility's energy requirements during those hours. The withdrawn,on-peak energy is a demand response event, and the net decrease inenergy use can be offered into the market and be compensated for as ademand response product. The stored energy is used by the facility inplace of utility-supplied energy when it is cheaper to use the storedenergy. The stored energy is added to the utility-supplied energy, andany excess stored energy over what is actually used can also be soldback to the grid.

In the case of energy storage using a battery, embodiments of theprocess according to the invention optimize the use of the battery toprovide the best economic result, including increased off peak usage,decreased on-peak usage, and receipt of payments for verifiable actionsbased on Locational Marginal Prices (“LMPs”).

As an example, a facility may include a building having a heating,ventilating and air-conditioning system (HVAC), as well as various otherelectric energy-consuming devices represented by other loads. Theseother loads can be, for example, lighting, electric motors, industrialheating equipment, refrigeration equipment, pumps, elevators, andelectronic equipment. Use of all of these devices can be optimized suchthat the total electric bill is minimized.

The facility may also include its own solar collector (or solargenerator), and a storage battery, connectible to the solar collectorthrough a switch. The storage battery may be switched from the solarcollector to an inverter, which can convert the direct current from thebattery to alternating current synchronized with 60 Hz electric currentdelivered to the facility through a local power line, which is connectedto a utility grid through a transformer. Operation of the battery andsolar generator can be co-optimized so as to maximize the return on bothof these assets, based upon the inputs and operating schedule developedby embodiments of the invention.

A dispatch center, which includes computers and a computer memory, incommunication with a controller at the facility through a communicationnetwork, and can send instructions such as set forth below.

An example of embodiments of the present inventions for managing energyat a facility may include the following scenario: First, according to amodel of the facility, the interior temperature of the building may benormally set at 72° F. during ordinary business hours, i.e., 7:00 AM to5:00 PM. At night, however, when the building is not in use, itsinterior temperature can be allowed to rise to 80° F.

Assume that the facility operator agrees that, during the hours from10:30 AM to 5:00 PM, the interior temperature within the building may beallowed to rise to only 75° F. rather than 80° F. By doing this, theoperator can reduce the building's energy bill.

In the example, the program, looking ahead at the weather prediction forthe next day, and having stored in its memory not only the facilitymodel and current information on energy rates, but also the informationthat 75° F. is an acceptable working hour temperature, determines thatthe least expensive way to maintain the desired working hour temperatureis to pre-cool the building during a time between midnight and 7:00 AM,when energy rates are lower than they are during the day. Pre-coolingcan reduce the building temperature to 73° F., which is somewhat lowerthan the desired 75° F. temperature using lower-cost electric energy,and reduce the consumption of energy during the day, thereby reducingthe net cost of energy consumed over a full day.

The net cost of energy can be reduced in an exemplary scenario. In thescenario, the ambient outside temperature varies from 90° F. at 6:00 PMto 80° F. at 6:00 AM. The ordinary operation of the HVAC system iswithout control by the computer at the dispatch center. It is assumedthat 75° F. is an acceptable daytime temperature. The interiortemperature is allowed to rise at night from 75° F. to 80° F. In themorning, soon after the interior temperature reaches 80° F., the HVACunit switches on, and begins to reduce the temperature to a levelslightly below 75° F. using electric energy at the daytime rate duringan interval from T1 to T2. During the day, the HVAC unit switches on andoff continually, maintaining the interior temperature within arelatively narrow temperature band averaging 75° F. Assuming that thepower consumption of the HVAC unit is constant, the cost of energyconsumed is proportional to the energy rate multiplied by the aggregateon-time of the HVAC unit.

In the example case where the off-site computer calls for pre-cooling,it determines when pre-cooling should commence based on the currentinterior temperature, the current ambient temperature, and the weatherprediction for the next day. Pre-cooling commences at time T3, e.g., atapproximately 4:00 AM, and continues until time T4, at which point theinterior building temperature is close to and approaching 73° F. TheHVAC unit switches on and off during the day to maintain a 75° F.temperature. The aggregate on-time of the HVAC unit during the day isless than the aggregate on-time in the absence of external control. Thetotal cost of electric energy for the HVAC unit during the 24 hourinterval from 6 PM to 6 PM the next day can be lower, because the costof electric energy is low from time T3 to time T4.

The computer at the dispatch center can also cause the controller tocharge the battery from the grid power or from the solar collector whenthe prices are low and discharge the battery to the grid when prices arehigh. The computer can also cause the solar collector to sell its powerto the grid when this is more economical than using the solar collectorto charge the battery.

FIG. 1 shows an embodiment of a method of the present invention.Historical data 102 from a customer's facility is inputted into thesystem to calculate the RTO system operator's estimate of the customerbaseline (CBL) 112 usage of electrical energy. This is the historicallyexpected customer use of electricity, if the energy optimization of thepresent invention is not applied, or calculated by the RTO operator.This CBL calculation 112 is inputted to the system to calculate a BAU(business as usual) electrical energy forecast 114 for the facility,which forecasts the energy usage if the energy management of the presentsystem is not used. Alternatively, an alternate customer baseline 130may be calculated, by the user of the present invention, in place of theRTO system operator's calculation of the customer baseline 112, to beused for the BAU load forecast 114. The system prepares data 116 so thatthe system may display an hourly default schedule and parameters 132based on the CBL. This default schedule 132 is then transmitted to theend user's of electricity at facilities 134. The end user 134preferences, objectives and constraints 136 for their energy use arethen input. These user preferences, objectives and constraints 136together with the data 116 from the CBL and BAU load forecasts 112, 114and the resulting data 116 are used to generate a renewable energyforecast 118. This renewable energy forecast 118 can then be recycled tocalculate an updated BAU load forecast 114.

The system also obtains a weather feed from the weather bureau 104 forinput into the calculation of the BAU load forecast 114.

The system also stores a default schedule and parameters 106 for thepreparation of data 116, as needed.

Embodiments of the invention may also obtain price forecasts from aservice bureau 108 to inform the calculation of an optimalgeneration/load schedule (unconstrained) 120. These schedules 120 areused to develop feeder schedules 138, which are delivered to the RTOgrid and facilities micro grid operators 140. The operator 140 thenprovides a feeder constraint or violations analysis 142 which is used bythe system to calculate an optimal generation/load schedule(constrained) 122. This schedule 122 is then used to calculate net costs(profits) 124 for application by embodiments of the system. These netcosts and profits 124 then are used to display the TREF (reference,temperature, i.e. the target temperature for the facility, load sheddingschedule and prices 144. These displays are then available to the endusers of the electricity and of the system 146. The end users 146 maythen accept or modify the user's preferences 148. These schedules may beaccepted 126 and those accepted schedules are transmitted 128 to thenext step and used to calculate a day ahead virtual generation schedule210, as shown as FIG. 2. If the schedules are not accepted 126, then theinput parameters are adjusted 110, and recalculation is performed on theoptimal generation/load schedules (constrained) 122, and the process isreiterated until all schedules are accepted at 126 and transmitted at128 to calculate a day ahead virtual generation schedule 210.

FIG. 2 shows further embodiments of a method of the present invention.Customer baseline CBL 202 is used to calculate the day ahead virtualgeneration schedule 210, which also uses the accepted schedules 126.This schedule data is then used to display hourly schedules, prices andcosts for the facility 212. The schedules 212 are used to show hourlyschedules, or other time period breakdowns, and prices and costs 224,which are transmitted to scheduler 226. Scheduler 226 may accept ormodify these schedules and prices 228. If the prices are accepted 214,then day ahead (DA) versus real-time (RT) bidding strategy 206 isdeveloped. If the schedules 224 are not accepted 214, then the systemadjusts inputs and reoptimizes the schedule at 204, to recalculate theday ahead virtual generation schedule 210 and the process is reiterateduntil the schedules 224 are accepted at 214. The bidding strategy 206results in a final bid and submission to the day ahead market 216. Thisgenerates a bid that is broken down by hourly, or other time period,schedules and prices 230, and sent to a market operator 232. The marketoperator 232 may accept or modify (by 4 PM) the finalized bid 216, at234. The system may then revise or modify (scheduler reviews andmodifies) 218 the accepted or modified bids. The system may adjustinputs and reoptimize the schedules in 208, if necessary. A reliabilityrun for revised bids may be submitted 220 (those bids that have not beenaccepted in the day ahead). These revised bids may result in revisedhourly schedules and prices 236, and then they are resubmitted to themarket operator 238. This may result in generation of final schedules222, which results in a generation of consumer reports 240 that aredelivered to the end users of electricity 242.

FIG. 3 shows a general overview of an embodiment of one possiblealgorithm of the present invention, to predict the customer baseline(“CBL”) load using parametric estimation. In general, complex processesare often described by non-linear equations, which present a challengeto the most advanced optimization engines. The objective of theembodiment is to provide an accurate CBL model represented by linearizedequations, where the coefficients of the linearized equations areadjusted in real-time from actual process measurements, using parametricestimation. This method may provide an accurate load forecast, withminimal effort necessary to deploy the solution. In addition, the modelmay be adapted over time to physical changes in the building, such asefficiency improvements.

Module 314 contains the linearized simulation model (Instance ‘A’) ofthe CBL load. The optimization engine (Module 316) will iterate on thecontrol vector u(t) which represents the control variables (e.g.,temperature control), and Module 314 will compute the correspondingstate vector x(t), which represents the process variables (e.g.,temperature, humidity) over time, taking into account various inputssuch as the weather forecast parameters (Module 308) and Price ForecastFeed 306. The optimization engine (Module 316) iterates on the controlvector u(t) until the objective function (Module 320) is optimized overthe defined time period (e.g., next 24 hours). The objective functioncombines several user adjustable components that include such factors ascomfort, economic benefit and environmental objectives, along withvarious time dependent constraints on both state and control variables.Economic benefits are derived from known time dependent economicparameters, including the wholesale price forecast (Module 308). Thesolution is stored in the Optimization Database (Module 304), whichcontains various optimization scenarios or cases that can be simulatedand compared by the end user. Once a case is selected, the user willthen commit the specific scenario to the Real-Time Database (Module302).

The selected control vector u(t) may then be used by the BuildingManagement System to control the corresponding physical resources as afunction of time. The same control vector is submitted in parallel tothe Module 312, which contains an identical version of the BuildingModel (Instance ‘B’). The output of Module 312, the simulated state ofthe building x(t), is compared against the measured state xm(t), whichis produced by the Building Management System 310. The difference is fedto the Parametric Estimator (Module 318) which calculates, by doingparametric estimation (e.g., using Kalman filters), the modelcoefficients A(t) and B(t) that minimize the difference between thesimulated state and the observed state of the building. Thesecoefficients are updated periodically to ensure that the linearizedmodel used for the optimization (Module 314) forecasts accurately thetime dependent simulated behavior of the building in the vicinity of thecurrent measured state.

FIG. 4 describes VPower system modules for embodiments of the presentinvention. Each module may be a software module executed on hardware.The modules are in electronic communication with each other as shown inFIG. 4 to operate in the system.

Model builder module 502 models a particular client facility such as abuilding or office campus. The energy resources modeled may include thebuilding, the lights, HVAC, motors, electricity generation capacity,generators, and solar generators, for example. The model builder module502 and other technical elements 502 through 542 shown in FIG. 4 mayvariously run as software on PCs or other appropriate computerfacilities.

The components library 503 may be a library of software components tomodel particular genres of facilities or energy resources, such asbuildings, HVAC and motors, which components 503 may be calibrated withspecific parameters for specific clients.

The generation forecast module 504 receives weather forecast informationfrom the weather forecast module 510, and receives electricity priceforecasts and related price forecasts, from the price forecast module512. The generation forecast module 504 then generates for the customera forecast of the customer's electricity generation, for example fromsolar or diesel generation.

The load forecast module 505 generates a forecast of load for a specificfacility, that is a forecast of the electricity demand used by thecustomer's facility, broken down for time periods over the day, forexample, in half hour increments.

The optimization module 506 produces optimum options for the individualcustomer, presenting tradeoffs for optimizing different parameters, suchas the total cost of electricity versus comfort from the maintainedtemperature. For example, there is a tradeoff in the summer monthsbetween the cool temperature generated within a facility by HVAC and theenergy costs to generate the same.

The power analytics module 507 analyses a micro electric grid (e.g. fora campus or a large building for a customer), and maintains acceptablepower quality within the forecasts and optimizations, for examplemaintaining required voltage and amps within acceptable tolerances.

The carbon calculator module 508 calculates how much the system optionspresented by optimization module 506 may each reduce the carbonfootprint of the client system.

The Viridity engineer 509 communicates with the various modules of thesystem 502 through 508 to develop the forecasts and other output on adaily basis, perhaps disaggregated by hourly or lesser time periods.

The modules 502, 503, 504, 505, 506, 507 and 508 communicate with eachother in the system and with the Viridity engineer 509 in theoptimization mode of the system to develop and analyze optimizationoptions for the target client facility.

The weather forecast module 510 provides data which is purchased fromoutside vendors and the data is imported to the generation forecastmodule 504.

The price forecast module 512 also provides data bought from outsidevendors to import price forecast data to the generation forecast module504.

The monitor and control module 520 monitors the client's facilities todetermine if the customer actually operates facilities in the manner ofthe chosen option and also communicates with the grid operator 524. Themonitor and control module 520 also can be used to remotely control theclient's facility 540, if authorized, and if in electronic communicationwith the client's facility 540.

The process interface module 522 is similar to a communication API inelectronic communication between the monitor and control module 520 andthe gateway 526.

Gateway 526 is a gateway in electronic communication with the EMS/BMSSCADA module 540. The energy management system (“EMS”), and the buildingmanagement system (“BMS”), and the supervisory control and dataacquisition (“SCADA”) system are legacy systems installed in thecustomer's facilities that communicate through the gateway 526 to theirprocess interface module 522 and to the modules 502 through 508 of theVPower system. The EMS/BMS SCADA module 540 communicates through thegateway 526 to the process interface 522 through a variety of possiblecommunication links including, e.g., the Internet, which links mayinclude a virtual private network (“VPN”).

The facility manager 542 communicates with and controls the EMS/BMSSCADA module 540 through communication through his computer system.

The market/utility interface module 524 communicates with the carboncalculator 508 and the other VPower system modules and the monitoringand control module 520. Furthermore the market/utility interface 524communicates with the market operating/utility 534 and the Viriditydispatcher 530. The Viridity dispatcher 530 communicates with thecustomer interface module 532. The customer interface module 532 permitsthe Viridity dispatcher 530 to communicate with the customer facilitymanager 542.

The facilities manager 542 offers to produce power at a price, or tocontrol load to an extent. If this is accepted by the marketoperator/utility 534 at a particular price, then the facility 540 mustconsequently perform accordingly.

The market/utility interface 524 is similar to an API that communicatesbetween the VPower system modules 502 through 508, and the marketoperator/utility 534, and the settlement module 528. The market/utilityinterface 524 communicates to the grid operator 534 that the Viriditydispatcher 530 makes an offer to the operator 534 on behalf of thefacility manager 542 to produce electricity at a price and a time and aquantity, or to reduce consumption from the CBL (consumer base line) ina certain amount at a certain time. The operator 534 may then acceptthat offer. This information is then transmitted to the settlementmodule 528 to monitor specific performance by the facility 540 toproduce electricity or reduce consumption from the CBL as agreed, and toarrange billing and payment accordingly between the market operator 534and the facility manager 542.

The monitor and control module 520, the process interface module 522,the gateway 526, the market/utility interface 524, the settlement module528, and the customer interface 532 are part of the real-time modeoperation of the system. In the real-time mode, these modules monitorand control what the facility is actually doing, and also inform thefacility manager 542 and the Viridity dispatcher 530 of sudden changesin prices that may lead to an alteration of the optimization schedule.

The weather forecast module 510 and the price forecast module 512 areowned and operated by third parties. The EMS/BMS/SCADA module 540 isowned and operated by the customer. The optimization mode modules502-508 are owned and operated by Viridity, as are the modules 520 and522.

The Viridity engineer 509, weather forecast module 510, price forecastmodule 534, market/utility interface 524, settlement module 528,Viridity dispatcher 530, customer interface 532, facility manager 542,EMS/BMS/SCADA 540, and gateway 526, may communicate with the system 502,503, 504, 505, 506, 507, 508, 520, 522 and with each other through theInternet, wirelessly, by leased lines, POTS, VPN, or other telecomlinks.

FIG. 5 shows an example of the VPower optimization mode output.Electricity energy consumption and production features of a customer'sfacilities are shown, such as HVAC 602, solar panels (2 megawatts) 604,battery (5 megawatts hours) 606, gas fueled generator (5 megawatts) 608,and diesel generator (5 megawatts) 610. These resources 602 through 610integrate over the power grid 612 with the larger RTO power grid whichis a source of imported power 614. Here the term “imported power” meanselectric power from the RTO brought into the customer's facility overthe power grid 612.

Various optimization options, produced by the optimization module 506 inFIG. 4, are shown in FIG. 5 in columns 621, 622, 623, 624, 625 and 626,and rows 630 through 646. Column 621 shows various row titles includingthe date row 630, temperature optimization in row 631, various powerproduction and consumption facilities in rows 632 through 636, beingrespectively solar, battery, gas generation, diesel generation, andfixed load (fixed power consumption or fixed demand).

Row 637 shows gas generation cost for the customer, and row 638 showsdiesel generation costs by the customer. Line 639 shows the retail nightcost of electricity from the customers supplier, and line 640 shows theretail day cost of electricity from the supplier. Line 641 shows thegeneration and transmission costs reflected in retail rates.

Line 642 shows the megawatt hours of imported electricity from the gridto the facility under different optimization scenarios. Line 643 showsthe supply cost savings. Line 644 shows the demand response (reduceddemand) revenue, i.e. the revenue paid to the facility operator by theRTO for the facility generator's reduction in the facility's energyusage below the CBL. Line 645 shows the fuel costs applicable and line646 shows the net savings for the cases illustrated in the optimizationexamples.

Column 622 shows various units and prices for the respective items incolumn 621. MW abbreviates megawatts. MWH abbreviates megawatt hours.

Line 630 through 636, in column 622, shows the production capacity ofthe respective facilities. Lines 637 through 641 of column 622 shows theprices of the various factors named in column 621.

The checkmarks in lines 631 through 636 in cases 0 through 3 in columns623 through 626 indicate what options are active in the indicatedoptimization case. Lines 637 through 646 in columns 623 through 626 showthe various indicated prices and costs of the various features andoptions selected in the various cases. Line 646 shows the financialbenefit of each option.

For example, in column 626 in optimization case 3 produced byembodiments of the invention, temperature is optimized, and all five ofthe energy resources including a solar, battery, gas generation, dieselgeneration and fixed load are implicated. The applicable prices areindicated in lines 637 through 641. The result in 642 is importing 63.64megawatt hours of electricity from the grid (rather than the 226.64 MWHin Case 0), with a supply savings of $9,893.02 in line 643, with demandreduction reimbursement from the grid to the facility in line 644 of$21,376.18, with a fuel cost to the facility in line 645 of $12,092.02,for a net savings to the customer in case 3 of $19,177.18 shown in line646. Of the four cases shown in this FIG. 5, the highest net savings inline 46 is with case 3, which is thereby indicated as the mostoptimizing case.

FIG. 6 shows the VPower system architecture for embodiments of theinvention that support and implement the modules shown in FIG. 4 toproduce the optimization example shown in FIG. 5. Appropriate computerand communications hardware and software is used in an integration layer704 to permit integrated communication between the portal 509,generation forecast module 504, the VP (VPower) load forecast CBL 505(the VPower load forecast of the “customer base load”), the poweranalytics module 507, the optimizing module 506, the model builder 502,the market interface 524, the VPower gateway 526, the settlement module528, the forecast data feeds 510, 512 from the external services 510,512, and the carbon footprint calculator 508. (The same element numbersare used in FIG. 6 and FIG. 4, where the same elements are referred toin both Figures.)

Furthermore, the integration layer 704 allows integrated communicationbetween these components and the PJM CBL calculator 702, the userinterface engine 706, the displays 708, and the SCADA, EMS, BMS, 540.CBL abbreviates “customer base load” for electric power and is discussedfurther herein. The PJM CBL calculator 702 is a CBL calculator providedby a specific RTO in the Northeast, that being PJM.

The VP load forecast CBL 505 is referred to in FIG. 4 as the loadforecast module 505. This is an alternative forecast of the CBL by aVPower embodiment of the invention. The PJM CBL calculator 702 may beused initially to forecast the CBL. However, it may be that thealternative VP load forecast CBL 505 provides a superior algorithm andmay eventually replace use of the PJM CBL calculator 702 to forecast theCBL. The system as indicated in FIG. 6 may use either or bothalternative CBL calculations 505, 702, to support the settlement module528.

The portal 509 is used by the Viridity engineer of 509 as indicated inFIG. 7 to access the embodiment.

The UI engine 706 may develop, project and support the user interfaces708 used by the Viridity engineer 509, the Viridity dispatcher 530, thefacility manager 542, and by the market operator utility 534. The UIengine 706 projects the displays 708 used by the various users.

FIG. 7 is a chart describing the operation of one possible optimizationoption that may be calculated by system modules of FIG. 4 through theoptimization examples in case 3 in FIG. 5 shown in column 626, using thesystem architecture of FIG. 6.

The horizontal axis shows time over a 24-hour cycle in 30-minuteintervals. The vertical axis on the left-hand scale shows megawatts, thevertical axis on the right-hand scale shows cost in dollars. Thedifferent vertical bars show the production of electricity by a facilityin option 3 at various times during the day, produced by dieselgeneration 810, gas generation 820, solar generation 830, power batterydischarge 840, imported electricity from the RTO power grid 850, and thelocational marginal price (LMP) throughout the day is shown in the line860. Hence, we can see that under this optimization scenario, forexample electricity imported from the grid 850 is maximized during thehours around 3:30 a.m. when the LMP is the lowest, and the electricityimported from the grid 850 is reduced to zero during the hours around15:00 hours when the LMP is highest.

Also, it appears that the facility may be pre-cooled during the timearound 3:30 hours when the LMP is lowest, by a substantial use ofimported electricity.

Also, is appears that total use of electricity is peaked again in thehours around 15:00 hours when the demand for cooling is highest in theafternoon. But at this time, imported power 850 is reduced to zerobecause the LMP 860 is most expensive. This is accomplished by usingdiesel generation 810, gas generation 820, solar generation 830 (whichis possible because the sun is out), and discharging the batteries 840.The batteries have been charged during the night around 3:30 hours whenthe LMP is lowest, to be discharged in the afternoon when the LMP ishighest.

In a similar manner, FIG. 7 shows the optimized use of each of theenergy resources throughout the 24-hour cycle.

Other Matters

As used herein, a “computer” or “computer system” may be, for exampleand without limitation, either alone or in combination, a personalcomputer (PC), server-based computer, main frame, server, microcomputer,minicomputer, laptop, personal data assistant (PDA), cellular phone,pager, processor, including wireless or wireline varieties thereof, orany other computerized device capable of configuration for receiving,storing or processing data for standalone application or over anetworked medium or media.

Computers and computer systems described herein may include operativelyassociated non-transitory computer-readable memory media such as memoryfor storing software applications used in obtaining, processing, storingor communicating data. It can be appreciated that such memory can beinternal, external, remote or local with respect to its operativelyassociated computer or computer system. Memory may also include anymeans for storing software or other instructions including, for exampleand without limitation, a hard disk, an optical disk, floppy disk, DVDcompact disc, memory stick, ROM (read only memory), RAM (random accessmemory), PROM (programmable ROM), EEPROM (extended erasable PROM), orother like computer-readable media.

In general, non-transitory computer-readable memory media may includeany medium capable of storage of an electronic signal representative ofdata stored, communicated or processed in accordance with embodiments ofthe present invention. Where applicable, method steps described hereinmay be embodied or executed as instructions stored on a non-transitorycomputer-readable memory medium or media.

It is to be understood that the figures and descriptions of embodimentsof the present invention have been simplified to illustrate elementsthat are relevant for a clear understanding of the present invention,while eliminating, for purposes of clarity, other elements. Those ofordinary skill in the art will recognize, however, that these and otherelements may be desirable. However, because such elements are well knownin the art, and because they do not facilitate a better understanding ofthe present invention, a discussion of such elements is not providedherein. It should be appreciated that the figures are presented forillustrative purposes and not as constriction drawings. Omitted detailsand modifications or alternative embodiments are within the purview ofpersons of ordinary skill in the art.

It can be appreciated that, in certain aspects of the present invention,a single component may be replaced by multiple components, and multiplecomponents may be replaced by a single component, to provide an elementor structure or to perform a given function or functions. Except wheresuch substitution would not be operative to practice certain embodimentsof the present invention, such substitution is considered within thescope of the present invention.

The examples presented herein are intended to illustrate potential andspecific implementations of the present invention. It can be appreciatedthat the examples are intended primarily for purposes of illustration ofthe invention for those skilled in the art. The diagrams depicted hereinare provided by way of example. There may be variations to thesediagrams or the operations described herein without departing from thespirit of the invention. For instance, in certain cases, method steps oroperations may be performed or executed in differing order, oroperations may be added, deleted or modified.

Furthermore, whereas particular embodiments of the invention have beendescribed herein for the purpose of illustrating the invention and notfor the purpose of limiting the same, it will be appreciated by those ofordinary skill in the art that numerous variations of the details,materials and arrangement of elements, steps, structures, or parts maybe made within the principle and scope of the invention withoutdeparting from the invention as described in the following claims.

Various components of embodiments of the invention may be implemented assoftware code to be executed by a processor of any computer system usingany type of suitable computer instruction type. The software code may bestored as a series of instructions or commands on a non-transitorycomputer readable memory medium. The term “non-transitorycomputer-readable memory medium” as used herein may include, forexample, magnetic and optical memory devices such as diskettes, compactdiscs of both read-only and writeable varieties, optical disk drives,and hard disk drives. A non-transitory computer-readable memory mediummay also include memory storage that can be physical, virtual,permanent, temporary, semi-permanent or semi-temporary.

The methods may be implemented by any suitable type of hardware (e.g.,device, computer, computer system, equipment, component), software(e.g., program, application, instruction set, code), storage medium(e.g., disk, device), propagated signal, or combination thereof.

Embodiments of the invention may be implemented utilizing any suitablecomputer languages (e.g., C, C++, Java, JavaS-cript, Visual Basic,VBScript, Delphi) and may be embodied permanently or temporarily in anytype of machine, component, physical or virtual equipment, storagemedium, or propagated signal capable of delivering instructions to adevice. These software applications, or computer programs may be storedon a computer readable medium (e.g., disk, device), such that when acomputer reads the medium, the functions described herein are performed.

In general, elements of embodiments may be connected through a networkhaving wired or wireless data pathways. The network may include any typeof delivery system including, but not limited to a local area network(e.g., Ethernet), a wide area network (e.g., the Internet and/or WorldWide Web), a telephone network (e.g., analog, digital, wired, wireless,PSTN, ISDN, and/or xDSL), a packet-switched network, a radio network, atelevision network, a cable network, a satellite network, and/or anyother wired or wireless communications network configured to carry data.The network may include elements, such as, for example, intermediatenodes, proxy services, routers, switches and adapters configured todirect or deliver data.

In general, elements of embodiments may include hardware or softwarecomponents for communicating with the network and with each other. Theseelements may be structured and arranged to communicate through thenetwork using various communication protocols (e.g., HTTP, TCP/IP, UDP,WAP, WiFi Bluetooth) or to operate within or in concert with one or moreother communications systems.

Elements of embodiments may include one or more servers (e.g. IBM®operating system servers, Linux operating system-based servers, WindowsNTT™ servers, Sybase) within the system.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made and that otherimplementations are within the scope of the following claims.

“VPowerSystem™” is a trademark of Viridity Energy, Inc.

1. A method for optimizing the use of electric energy by a facilitycomprising a plurality of energy assets, the method comprising: (a)modeling, using a computer, the usage of electric energy by thefacility, by creating a simulation model of the facility and calculatinga customer baseline (“CBL”); (b) storing the model in the computer; (c)determining, using the computer, a plan for controlling the operation ofat least one of the facility's energy assets to reduce the electricityprovider's overall charge to the facility for electric energy, or toprovide a revenue source to the facility, the determining being done onthe basis of the model, and variations in the price of electric energyduring a day; (d) generating, using the computer, control signals basedon the plan from data derived from the computer; and (e) controlling,using the generated control signals, at least one of the facility'senergy assets so that operation of the facility conforms to the plan. 2.The method of claim 1, wherein the energy assets comprise at least oneof: generation, storage, usage, and controllable load assets.
 3. Themethod of claim 1, wherein the step of determining a plan forcontrolling the operation of at least one of the facility's energyassets is also based on weather prediction data provided to the computerand price prediction data provided to the computer.
 4. The method ofclaim 1, in which said at least one of the facility's energy assets is acontrollable air conditioning unit.
 5. The method of claim 1, in whichthe facility further comprises an energy storage device and a solargenerator connected to the energy storage device, the method furthercomprising: (a) controlling the use of the solar generation by a controlsignal automatically generated from electric data derived from thecomputer, to charge the energy storage device with solar generationduring a time when the price of energy is below first predeterminedlevel, and (b) controlling the energy storage device by a control signalautomatically generated from electronic data derived from the computer,to discharge the energy storage device to the utility power grid whenthe price of energy charged to the facility by the energy provider isabove a second predetermined level.
 6. The method of claim 1, in whichthe facility further comprises an energy storage device connected to autility power grid, the method further comprising (a) controlling theenergy storage device by control signals automatically generated fromelectronic data derived from the computer to charge the energy storagedevice when the price of energy is below a first predetermined level,and (b) controlling the energy storage device by control signalsautomatically generated from electronic data derived from the computerto discharge the energy storage device to the utility power grid whenthe price of energy charged is above a second predetermined level.
 7. Acomputer system for optimizing use of electric energy by a facilitycomprising a plurality of energy assets, the system programmed toexecute a method comprising: (a) modeling, using a computer, the usageof electric energy by the facility, by creating a simulation model ofthe facility and calculating a customer baseline (“CBL”); (b) storingthe model in the computer; (c) determining, using the computer, a planfor controlling the operation of at least one of the facility's energyassets to reduce the electricity provider's overall charge to thefacility for electric energy, or to provide a revenue source to thefacility, the determining being done at least on the basis of the model,and variations in the price of electric energy during a day; (d)generating, using the computer, control signals based on the plan fromdata derived from the computer; and (e) controlling, using the generatedcontrol signals, at least one of the facility's energy assets so thatoperation of the facility conforms to the plan.
 8. The computer systemof claim 7, wherein the energy assets comprise at least one of:generation, storage, usage, and controllable load assets.
 9. Thecomputer system of claim 7, wherein the step of determining a plan forcontrolling the operation of at least one of the facility's energyassets is also based on weather prediction data provided to the computerand price prediction data provided to the computer.
 10. The computersystem of claim 7, in which said at least one of the facility's energyassets is a controllable air conditioning unit.
 11. The computer systemof claim 7, in which the facility further comprises an energy storagedevice and a solar generator connected to the energy storage device, themethod further comprising: (a) controlling the use of the solargeneration by a control signal automatically generated from electricdata derived from the computer, to charge the energy storage device withsolar generation during a time when the price of energy is below a firstpredetermined level, and (b) controlling the energy storage device by acontrol signal automatically generated from electronic data derived fromthe computer, to discharge the energy storage device to the utilitypower grid when the price of energy charged to the facility by theenergy provider is above a second predetermined level.
 12. The computersystem of claim 7, in which the facility further comprises an energystorage device connected to a utility power grid, the method furthercomprising (a) controlling the energy storage device by control signalsautomatically generated from electronic data derived from the computerto charge the energy storage device when the price of energy is below afirst predetermined level, and (b) controlling the energy storage deviceby control signals automatically generated from electronic data derivedfrom the computer to discharge the energy storage device to the utilitypower grid when the price of energy charged is above a secondpredetermined level. 13-18. (canceled)
 19. A computer system foroptimizing the use of electric energy by a facility comprising energyassets, the system comprising: (a) a model builder module; (b) acomponents library module; (c) a generation forecast module; (d) a loadforecast module; (e) an optimization module; (f) a power analyticsmodule; and (g) a carbon calculation module. wherein all of the modulesare in electronic communication with each other.
 20. The system in claim19, further comprising: (a) a monitor and control module, and (b) aprocess interface module.
 21. The system in claim 20, adapted toelectronically communicate with: (a) a weather forecast module; (b) aprice forecast module; (c) a market/utility interface, adapted tocommunicate with a settlement module, and (d) a gate adapted tocommunicate with at least one of an EMS, BMS, and SCAD A.
 22. The systemin claim 20, wherein the model builder module is configured to create asimulation model of usage of electric energy by the facility and tocalculate a customer baseline (“CBL”).